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| -rw-r--r-- | ml/dlib/examples/svr_ex.cpp | 96 |
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diff --git a/ml/dlib/examples/svr_ex.cpp b/ml/dlib/examples/svr_ex.cpp new file mode 100644 index 000000000..a18edf24d --- /dev/null +++ b/ml/dlib/examples/svr_ex.cpp @@ -0,0 +1,96 @@ +// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt +/* + This is an example illustrating the use of the epsilon-insensitive support vector + regression object from the dlib C++ Library. + + In this example we will draw some points from the sinc() function and do a + non-linear regression on them. +*/ + +#include <iostream> +#include <vector> + +#include <dlib/svm.h> + +using namespace std; +using namespace dlib; + +// Here is the sinc function we will be trying to learn with the svr_trainer +// object. +double sinc(double x) +{ + if (x == 0) + return 1; + return sin(x)/x; +} + +int main() +{ + // Here we declare that our samples will be 1 dimensional column vectors. + typedef matrix<double,1,1> sample_type; + + // Now we are making a typedef for the kind of kernel we want to use. I picked the + // radial basis kernel because it only has one parameter and generally gives good + // results without much fiddling. + typedef radial_basis_kernel<sample_type> kernel_type; + + + std::vector<sample_type> samples; + std::vector<double> targets; + + // The first thing we do is pick a few training points from the sinc() function. + sample_type m; + for (double x = -10; x <= 4; x += 1) + { + m(0) = x; + + samples.push_back(m); + targets.push_back(sinc(x)); + } + + // Now setup a SVR trainer object. It has three parameters, the kernel and + // two parameters specific to SVR. + svr_trainer<kernel_type> trainer; + trainer.set_kernel(kernel_type(0.1)); + + // This parameter is the usual regularization parameter. It determines the trade-off + // between trying to reduce the training error or allowing more errors but hopefully + // improving the generalization of the resulting function. Larger values encourage exact + // fitting while smaller values of C may encourage better generalization. + trainer.set_c(10); + + // Epsilon-insensitive regression means we do regression but stop trying to fit a data + // point once it is "close enough" to its target value. This parameter is the value that + // controls what we mean by "close enough". In this case, I'm saying I'm happy if the + // resulting regression function gets within 0.001 of the target value. + trainer.set_epsilon_insensitivity(0.001); + + // Now do the training and save the results + decision_function<kernel_type> df = trainer.train(samples, targets); + + // now we output the value of the sinc function for a few test points as well as the + // value predicted by SVR. + m(0) = 2.5; cout << sinc(m(0)) << " " << df(m) << endl; + m(0) = 0.1; cout << sinc(m(0)) << " " << df(m) << endl; + m(0) = -4; cout << sinc(m(0)) << " " << df(m) << endl; + m(0) = 5.0; cout << sinc(m(0)) << " " << df(m) << endl; + + // The output is as follows: + // 0.239389 0.23905 + // 0.998334 0.997331 + // -0.189201 -0.187636 + // -0.191785 -0.218924 + + // The first column is the true value of the sinc function and the second + // column is the output from the SVR estimate. + + // We can also do 5-fold cross-validation and find the mean squared error and R-squared + // values. Note that we need to randomly shuffle the samples first. See the svm_ex.cpp + // for a discussion of why this is important. + randomize_samples(samples, targets); + cout << "MSE and R-Squared: "<< cross_validate_regression_trainer(trainer, samples, targets, 5) << endl; + // The output is: + // MSE and R-Squared: 1.65984e-05 0.999901 +} + + |